README.md

metadata

title: Chronic Wound Classifier
emoji: 🩺
colorFrom: red
colorTo: blue
sdk: gradio
sdk_version: 5.49.1
python_version: '3.11'
app_file: app.py
pinned: false
license: mit

Chronic Wound Classifier — 4-class AZH demo

Demo classifier for chronic wound photographs: predicts one of four wound types (diabetic ulcer, pressure ulcer, surgical wound, venous ulcer) from an uploaded image.

Not a medical device. Not for clinical use. Research demonstration only.

Headline metric

Top-1 accuracy on the held-out AZH Test set (n=184): 0.8152 (cv_baseline_fold5_best.pt — the highest single-fold checkpoint from patient-grouped 10-fold cross-validation).

The 10-fold soft-vote ensemble of the same recipe scores 0.7989 on the same set; the single-checkpoint variant is shipped here for inference latency and footprint reasons.

Architecture

EfficientNet-B0 (ImageNet-pretrained), two-phase fine-tune (head-only 5 epochs at lr=1e-3, then full network 15 epochs at lr=1e-4). Patient-grouped CV splits ensure the same patient's images never appear in both train and val.

Limitations

• Pressure-class accuracy is ~0.41 — interpret pressure-class predictions with care.
• No fairness audit across skin tones (known gap).
• English-only UI; no mobile or offline build.
• Not validated on real patient cohorts outside AZH.

Source code & training pipeline

The training, evaluation, and methodology code live in the project repo: github.com — wound-classification (full link to be added by user)

Citation

Anisuzzaman et al. 2022. Multi-modal wound classification using wound image and location by deep neural network. Sci. Rep. 12:20057.